TidyTuesday Section (optional)

ImportantInstructions

You can count work on this week’s TidyTuesday toward the exceptional work required for an A in the Homework component.

Explore the week’s TidyTuesday challenge. Develop a research question, then answer it through a short data story with effective visualization(s). Provide sufficient background for readers to grasp your narrative.

Code
library(tidyverse)
edible_plants <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-02-03/edible_plants.csv')

How do water requirements relate to nutrient density and caloric content among edible plants?

Code
edible_clean <- edible_plants %>% 
  mutate(
    water = str_to_title(str_trim(water)),
    nutrients = str_to_title(str_trim(nutrients))
  ) %>% 
  filter(!is.na(water), !is.na(nutrients)) %>%   # ← THIS removes NA rows
  mutate(
    water = factor(water, 
                   levels = c("Very Low", "Low", "Medium", "High", "Very High"),
                   ordered = TRUE),
    nutrients = factor(nutrients,
                       levels = c("Low", "Medium", "High"),
                       ordered = TRUE)
  )

# edible_clean %>% 
#   count(water, nutrients)
Code
#data cleaning
energy_summary <- edible_clean %>% 
  group_by(water) %>% 
  summarise(mean_energy = mean(energy, na.rm = TRUE))
Code
edible_clean %>%
ggplot(aes(x = water, fill = nutrients)) +
  geom_bar(position = "fill", na.rm = TRUE) +
  geom_text(
    data = energy_summary,
    aes(x = water, y = 1.05, 
        label = paste0("Avg kcal: ", round(mean_energy, 1))),
    inherit.aes = FALSE) +
  scale_y_continuous(labels = scales::percent_format(),
                     limits = c(0, 1.1)) +
  labs(
    title = "Water Needs, Nutrient Density, and Energy Content",
    x = "Water Requirement",
    y = "Proportion of Plants",
    caption = "Source: TidyTuesday Week 5 (2026) – Edible Plants dataset. Author: David Rios.",
    fill = "Nutrient Level") +
  theme_minimal() +
  theme(plot.title.position = "plot")

Stacked proportional bar chart showing the distribution of nutrient levels (low, medium, high) across water requirement categories (very low to very high).

Fig. 1: Distribution of nutrient levels by water requirement, with average caloric content shown above each category.
Code
edible_collapsed <- edible_clean %>% 
  mutate(
    water = fct_collapse(
      water,
      Low = c("Very Low", "Low"),
      Medium = "Medium",
      High = c("High", "Very High")
    )
  )


energy_summary <- edible_collapsed %>% 
  group_by(water) %>% 
  summarise(mean_energy = mean(energy, na.rm = TRUE))
Code
ggplot(edible_collapsed, aes(x = water, fill = nutrients)) +
  geom_bar(position = "fill") +
  geom_text(
    data = energy_summary,
    aes(x = water, y = 1.05,
        label = paste0("Avg kcal: ", round(mean_energy, 1))),
    inherit.aes = FALSE
  ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(0, 1.1)
  ) +
  labs(
    title = "Water Needs, Nutrient Density, and Energy Content",
    subtitle = "Some extreme water categories were collapsed",
    x = "Water Requirement",
    y = "Proportion of Plants",
    fill = "Nutrient Level",
    caption = "Source: TidyTuesday Week 5 (2026) – Edible Plants dataset. Author: David Rios."
  ) +
  theme_minimal() +
  theme(plot.title.position = "plot")

Stacked proportional bar chart showing nutrient level distribution (low, medium, high) across three water requirement categories (low, medium, high). Average caloric content in kilocalories is displayed above each bar.

Fig. 1: Nutrient level distribution by collapsed water requirement categories, with average caloric content shown above each group.

Conclusion

Plants requiring higher water levels tend to have a greater share of high nutrient classifications, while low-water plants are more likely to be low nutrient. However, caloric content does not increase consistently with water demand, suggesting that nutrient density and energy content may not move together.